CN108982409A - A method of quickly detecting three constituent content of kelp lignocellulosic based near infrared spectrum - Google Patents
A method of quickly detecting three constituent content of kelp lignocellulosic based near infrared spectrum Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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Abstract
A method of it quickly detecting three constituent content of kelp lignocellulosic based near infrared spectrum, belongs to seaweed biomass detection field, comprising the following steps: (1) collection and pretreatment of kelp sample；(2) acquisition of kelp sample near infrared spectrum data；(3) wet chemical analytical method of three constituent content of lignocellulosic measures in kelp sample；(4) foundation and verifying of the quick detection model of near-infrared；(5) application of the quick detection model of near-infrared.The present invention can be used for three constituent content of lignocellulosic quickly, in Accurate Determining kelp, meet requirement high-throughput in actual production detection, and compared with existing lignocellulosic chemical analysis method, this method has many advantages, such as easy to operate, environmentally protective.
Description
Technical field
The present invention relates to seaweed biomass detection fields, particularly, are related to one kind and are quickly detected greatly based near infrared spectrum
The method of three constituent content of type brown alga lignocellulosic.
Background technique
The environmental degradations such as greenhouse effects problem and energy crisis make to develop the modern energy system based on renewable energy
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This, the content of three component of lignocellulosic has significantly to by the yield of the bio-fuel technique of raw material and quality of kelp
It influences, the content for rapidly and accurately measuring lignocellulosic in kelp is very necessary.
Existing kelp lignocellulosic content analysis method is broadly divided into conventional wet chemical analytic approach and modern times
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Grain and feed industries, 2005 (08): 40-41.；Document 22: Wang Jinzhu, Wang Yuanxiu, Li Feng, GAO YANHUA, Xu Junqing, Yuan Jian
The Shandong measurement [J] the food fermentation of cellulose, hemicellulose and lignin, 2010 (03): 44-47. in state's corn stover；Text
Offer 23: Jiang Yuan beautiful woman's Sargassum horneri base bio-ethanol preparation and algae-residue comprehensive utilizating research [D] Zhejiang Polytechnical University, 2015.) etc. side
Method.Wherein, the component detection range of ramie chemical component quantitative analysis method is more comprehensive, but is primarily adapted for use in bast-fibre
Detection, finds that this method is not fully applicable in the actually detected case to other plant material components, especially to wooden fibre
The detection of plain three components is tieed up, this method is measured by the summation that all the components total content subtracts other each component contents, accidentally
Difference is larger；Van Soest method and its improved method need to use neutral detergent to be handled when detecting wood fibre cellulose content,
This will lead to partially protein and lipid not can be removed completely, enable measurement result there are error, improved method use protease into
Row pretreatment, enables experimental implementation increasingly complex, inefficiency although improving accuracy；Paper making raw material chemical constituents determination mark
Standard is all independent from each other the measurement of each component, and measurement result is relatively accurate, but this method is only capable of measurement lignin and comprehensive fibre
Cellulose content is tieed up, hemicellulose and content of cellulose can not be measured；Similarly there is detecting step in other wet chemical methods
It is cumbersome, the disadvantages of taking a long time.Modern instrumental analysis method then mainly includes gas chromatography (GC) (reference literature 24: Zhang Hong
It is unrestrained, Zheng Rongping, Chen Jingwen, content [J] assay office of yellow and .NREL method measurement lignocellulosic material component, 2010,
29 (11): 15-18) and high performance liquid chromatography (HPLC, number of patent application: CN201310593952.4), chromatography detection
As a result more accurate, but the same complex and testing cost of detecting step is expensive.
Near infrared absorption wavelength of all kinds of identical or different groups in different chemical environments as contained by substance
(700-2526nm) and intensity have significant difference, therefore near infrared spectrum contains detection substance structure abundant and composition letter
Breath, wherein the bands of a spectrum most often observed are the absorptions of hydric group (C-H, N-H, O-H), therefore near infrared spectrum is very suitable to use
In the measurement of hydrogeneous organic substance (such as agricultural product, petroleum chemicals and drug) physico-chemical parameter.But Near-infrared Spectral Absorption peak is big
Mostly be the frequency multiplication and sum of fundamental frequencies absorption peak that middle infrared spectrum fundamental frequency absorbs, this enable near infrared spectrum show absorption intensity is weak, bands of a spectrum
The disadvantages of width and not strong overlapping serious and characteristic, reference literature 25: the small vertical chemometrics method of Chu and molecular spectrum point
Beijing analysis technology [M]: Chemical Industry Press, 2011:2-12..And Chemical Measurement is one by statistics or mathematics side
Method establishes the subject contacted between the chemical measurements of system and its state, is spread out by near infrared spectrum combination Chemical Measurement
The near infrared spectroscopic method born is needed for one kind can be extracted in weak and numerous and complicated near infrared spectrum from information strength
Key message realizes the method to the qualitative or quantitative analysis of unknown sample by founding mathematical models, has analysis quickly letter
Just, it is excellent that analysis object is lossless, analytic process is environmentally friendly, analysis result is accurate, analysis cost is cheap, live on-line analysis can be achieved etc.
Point, reference literature 26: Yao Wanqing, Peng Mengxia, Liu Ting near infrared spectrum combination chemometrics method answering in chemical analysis
With [J] Jiaying College journal, 2018,36 (05): 17-27..
Currently, near infrared spectroscopic method is mostly timber (patent application in the application of lignocellulosic context of detection
Number: CN201610936185.6), bamboo wood (number of patent application: CN200610099486.4), rape stem (number of patent application:
CN201710438483.7), corn (number of patent application: CN201511023790.6), tobacco leaf (number of patent application:
The lands source biomass such as CN201510047433.7), and for three component of tangleweed (especially kelp) lignocellulosic
The research of the rapid detection method of content is more rarely seen.Since the marine biomass such as tangleweed remove lignocellulosic group exceptionally,
Remaining main active substances (such as phycocolloid, sterols and terpenoid etc.) and land source biomass have relatively big difference, existing
Land source biomass near infrared spectroscopic method is difficult to directly apply to the marine biomass wood fibre cellulose content such as tangleweed
Detection.
Summary of the invention
Contain in view of conventional wet chemical quantitative analysis method and modern chromatographic method in three component of kelp lignocellulosic
Measure it is fixed present in disadvantage, the purpose of the present invention is to provide one kind based near infrared spectrum, and quickly to detect kelp wooden
The method of cellulose iii constituent content, realize it is quick to three constituent content of kelp lignocellulosic lossless, low in cost and
As a result accurate detection.
The technical solution adopted by the present invention to solve the technical problems is:
A method of quickly detecting three constituent content of kelp lignocellulosic based near infrared spectrum, including following
Step:
The first step, the collection and pretreatment of kelp sample
Acquire the multiple batches of different waters kelp sample, with clear water wash to eliminate surface attachment silt and
It is placed in preliminarily dried under sunlight after salinity, is then further dried in baking oven, most uses multi-deck screen after pulverizer crushes afterwards
Mesh screen point, takes 60 mesh brown alga particles to be fitted into spare in the sealed transparent bag vacuumized；
Second step, the acquisition of kelp sample near infrared spectrum data
The rotating sample pool that kelp particle is placed near infrared spectrometer is weighed, fills and strikes off, under diffusing reflection mode
Spectroscopic data is acquired, spectrometer scanning times are 64, resolution ratio 8cm-1, scanning wave-number range is 12000~4000cm-1, often
The near infrared spectrum data repeated acquisition of a sample is multiple, takes the average value of multiple spectroscopic data as the original of corresponding brown alga sample
Beginning near infrared spectrum；
Third step, the wet chemical analytical method measurement of three constituent content of lignocellulosic in kelp sample
Using the content of cellulose of improved sulfuric acid and potassium dichromate oxidation measurement kelp sample, using hydrolysis
Method measures the hemicellulose level of kelp sample, is measured using National Renewable Energy laboratory NREL method large-scale
The content of lignin of brown alga sample；
4th step, the foundation and verifying of the quick detection model of near-infrared
Firstly, being contained by three component of lignocellulosic that the near-infrared original spectral data that second step acquires is measured with third step
It measures data and constitutes total sample set, the identification and rejecting of exceptional sample are carried out to total sample set, remaining sample set uses Kennard-
Stone method is divided into calibration set, verifying collection and forecast set.It is then based on calibration set and verifying collects, to near-infrared original spectrum
Data carry out Pretreated spectra and characteristic wavelength screens, and it is close to use Partial Least Squares (PLS) to establish content of cellulose respectively
Infrared quick detection model, the quick detection model of hemicellulose level near-infrared, the quick detection model of content of lignin near-infrared,
It is finally based on forecast set, institute's quick detection model of near-infrared is verified, wherein using coefficient R, root-mean-square error
The evaluation index of Rmsec, relation analysis error RPD as model prediction performance；
5th step, the application of the quick detection model of near-infrared
Unknown kelp sample to be measured is subjected to sample pretreatment according to the first step, then according to described second
Step acquires corresponding near infrared spectrum data, and it is quick that near infrared spectrum data is inputted established near-infrared in the 4th step
In detection model, the three constituent content data of lignocellulosic of kelp sample to be measured can be obtained.
Preferably, in the 4th step, the preferred parameter of the built quick detection model of content of cellulose near-infrared is as follows:
(a) preprocessing procedures are Savitzky-Golay convolution First derivative spectrograply (1st)+remove Trend Algorithm, wherein
The parameter of Savitzky-Golay convolution First derivative spectrograply are as follows: window width 331cm-1, fitting of a polynomial order is 3；It goes
The parameter of gesture algorithm are as follows: fitting of a polynomial order is 1；
(b) characteristic wavelength screening technique is interval partial least square (IPLS)+genetic algorithm (GA), and modeling wave band is
4930~4560cm-1With 5820~5640cm-1；
(c) number of main factor selected by Partial Least Squares (PLS) is 10.
Either, in the 4th step, the preferred parameter of the built quick detection model of hemicellulose level near-infrared is as follows:
(a) preprocessing procedures are Savitzky-Golay convolution First derivative spectrograply (1st), wherein Savitzky-
The parameter of Golay convolution First derivative spectrograply are as follows: window width 201cm-1, fitting of a polynomial order is 3；
(b) characteristic wavelength screening technique is interval partial least square (IPLS), and modeling wave band is 5643~5340cm-1；
(c) number of main factor selected by Partial Least Squares (PLS) is 15.
Again either, in the 4th step, the preferred parameter of the built quick detection model of content of lignin near-infrared is as follows:
(a) preprocessing procedures are Savitzky-Golay convolution exponential smoothing, wherein Savitzky-Golay convolution is flat
The parameter of sliding method are as follows: window width 231cm-1, fitting of a polynomial order is 3；
(b) characteristic wavelength screening technique is correlation coefficient process, and modeling wave band is 5377~4824cm-1And 7405~
6194cm-1；
(c) number of main factor selected by Partial Least Squares (PLS) is 15.
The beneficial effects of the invention are that: described one kind is based near infrared spectrum and quickly detects kelp lignocellulosic
The method of three constituent contents introduces NIR technology in the detection of kelp component, realizes to lignocellulosic
The quick measurement of three components not only has compared with conventional wet chemical quantitative analysis method and modern chromatographic method and detects quick, knot
The advantages that fruit is accurate, process is environmentally friendly, while being conducive to be promoted in existing biomass conversion processes to kelp biomass components
The quality control level of content can also be applied in the quality control of other seaweed or even marine biomass.
Detailed description of the invention
Fig. 1 is Sargassum horneri particle near-infrared original spectrum scanning figure；
Fig. 2 is Sargassum horneri particulate fibrous cellulose content chemical analysis Distribution value figure；
Fig. 3 is Sargassum horneri particulate fibrous cellulose content chemical analysis value figure related to near-infrared model predicted value；
Fig. 4 is Sargassum horneri particulate fibrous cellulose content chemical analysis value figure compared with near-infrared model predicted value；
Fig. 5 is Sargassum horneri particle hemicellulose level chemical analysis Distribution value figure；
Fig. 6 is Sargassum horneri particle hemicellulose level chemical analysis value figure related to near-infrared model predicted value；
Fig. 7 is Sargassum horneri particle hemicellulose level chemical analysis value figure compared with near-infrared model predicted value；
Fig. 8 is Sargassum horneri ligno cellulose content chemical analysis Distribution value figure；
Fig. 9 is Sargassum horneri ligno cellulose content chemical analysis value figure related to near-infrared model predicted value；
Figure 10 is Sargassum horneri ligno cellulose content chemical analysis value figure compared with near-infrared model predicted value.
Specific embodiment
Embodiments of the present invention are further described below with reference to accompanying drawings and embodiments, and selected in embodiment
Kelp be Sargassum horneri.
Referring to Fig.1~Figure 10, one kind quickly detecting three constituent content of kelp lignocellulosic based near infrared spectrum
Method, comprising the following steps:
The first step, the collection and pretreatment of kelp sample
Acquire the multiple batches of different waters kelp sample, with clear water wash to eliminate surface attachment silt and
It is placed in preliminarily dried under sunlight after salinity, is then further dried in baking oven, most uses multi-deck screen after pulverizer crushes afterwards
Mesh screen point, takes 60 mesh brown alga particles to be fitted into spare in the sealed transparent bag vacuumized；
Second step, the acquisition of kelp sample near infrared spectrum data
The rotating sample pool that kelp particle is placed near infrared spectrometer is weighed, fills and strikes off, under diffusing reflection mode
Spectroscopic data is acquired, spectrometer scanning times are 64, resolution ratio 8cm-1, scanning wave-number range is 12000~4000cm-1, often
The near infrared spectrum data repeated acquisition of a sample is multiple, takes the average value of multiple spectroscopic data as the original of corresponding brown alga sample
Beginning near infrared spectrum；
Third step, the wet chemical analytical method measurement of three constituent content of lignocellulosic in kelp sample
Using the content of cellulose of improved sulfuric acid and potassium dichromate oxidation measurement kelp sample, using hydrolysis
Method measures the hemicellulose level of kelp sample, is measured using National Renewable Energy laboratory NREL method large-scale
The content of lignin of brown alga sample；
4th step, the foundation and verifying of the quick detection model of near-infrared
Firstly, being contained by three component of lignocellulosic that the near-infrared original spectral data that second step acquires is measured with third step
It measures data and constitutes total sample set, the identification and rejecting of exceptional sample are carried out to total sample set, remaining sample set uses Kennard-
Stone method is divided into calibration set, verifying collection and forecast set.It is then based on calibration set and verifying collects, to near-infrared original spectrum
Data carry out Pretreated spectra and characteristic wavelength screens, and it is close to use Partial Least Squares (PLS) to establish content of cellulose respectively
Infrared quick detection model, the quick detection model of hemicellulose level near-infrared, the quick detection model of content of lignin near-infrared,
It is finally based on forecast set, institute's quick detection model of near-infrared is verified, wherein using coefficient R, root-mean-square error
The evaluation index of Rmsec, relation analysis error RPD as model prediction performance；
5th step, the application of the quick detection model of near-infrared
Unknown kelp sample to be measured is subjected to sample pretreatment according to the first step, then according to described second
Step acquires corresponding near infrared spectrum data, and it is quick that near infrared spectrum data is inputted established near-infrared in the 4th step
In detection model, the three constituent content data of lignocellulosic of kelp sample to be measured can be obtained.
Embodiment 1: Sargassum horneri content of cellulose rapid detection method, process are as follows:
(1) collection and pretreatment of kelp sample
40 groups of Sargassum horneris of the multiple batches of different waters are acquired as embodiment sample, sample is by clear water washing until surface
The silt and salinity of attachment are all divided, and are subsequently placed to simply be air-dried under sunlight, are then dried, will wash at 105 DEG C
Drying Sargassum horneri sample pulverizer after net is crushed and is passed through multilayer screen cloth, and 60 mesh samples is taken to be packed into the sealing for evacuating air
It is spare in transparent bag.
(2) acquisition of kelp sample near infrared spectrum data
Weigh the rotating sample pool that kelp particle 4g is placed near infrared spectrometer, fill and strike off, by with Nicolet
IS10 Fourier Transform Near Infrared instrument (Thermo Fisher company of the U.S.) is the same as the near infrared spectrum of precision or higher precision
Instrument acquires spectrum, scanning times 64, resolution ratio 8cm under diffusing reflection mode-1, when scanning environment temperature keep constant (5~
25 DEG C), humidity is lower than 25%, and sample answers epigranular and sufficiently drying, and instrument sets automatic collection background spectrum, and spectrometer is swept
Retouching wave-number range is 12000~4000cm-1, the near infrared spectrum data repeated acquisition of each sample 3 times takes 3 spectroscopic datas
Original near infrared spectrum of the average value as corresponding brown alga sample.40 groups of Sargassum horneri particle near-infrared original spectrum scanning figures are shown in attached
Fig. 1.
(3) the wet chemical analytical method measurement of kelp content of cellulose
Wet chemical analytical method uses improved sulfuric acid and potassium dichromate oxidation, and method detailed step is as follows: weighing
0.2g (± 0.0001g) Sargassum horneri particle is placed in 100mL conical flask, be added 5mL glacial acetic acid and nitric acid mixed liquor (volume ratio 1:
1), glass stopper is placed in the water-bath boiled and heats 25min beyond the Great Wall, and is stirred continuously；Taking-up is filtered after being cooled to room temperature, and is discarded
Filtrate collects and all precipitates and be washed with distilled water 3 times；Precipitating is placed in 100mL conical flask, 10mL matter is added into precipitating
The potassium bichromate solution for measuring sulfuric acid solution and 10mL 0.1mol/L that score is 10%, shakes up and is placed in the water-bath boiled
Heat 10min；10mL distilled water is added, after solution is cooled to room temperature, KI solution and 1mL that 5mL mass fraction is 20% is added
The starch solution that mass fraction is 0.5% is titrated with the sodium thiosulfate of 0.2mol/L after shaking up, and is with 10mL mass fraction
The potassium bichromate solution of 10% sulfuric acid solution mixing 10mL 0.1mol/L is titrated as blank sample.Content of cellulose is pressed
Following formula calculates:
In formula: X represents content of cellulose, %；K represents the concentration of hypo solution, mol/L；A represents blank titration
The volume of consumed hypo solution, mL；B represents the volume of hypo solution consumed by solution, mL.
40 groups of Sargassum horneri particulate fibrous cellulose content chemical analysis Distribution values are shown in attached drawing 2.
(4) foundation and verifying of the quick detection model of near-infrared
Firstly, by the near-infrared original spectral data of step (2) acquisition and three component of lignocellulosic of step (3) measurement
Content data constitutes total sample set, and the identification and rejecting of exceptional sample are carried out to total sample set, and the exceptional sample method of inspection includes
Mahalanobis distance method, concentration residual error method and Principal Component Analysis.Finally, 40 groups of Sargassum horneri samples are rejected after exceptional sample identifies
3 samples choose 24 groups of samples based on Kennard-Stone (K-S) method and are used as calibration set, 9 groups of samples as verifying collect with
And 4 groups of samples are as forecast set.
Above-mentioned calibration set and verifying collection spectroscopic data are imported 8.3 software of matlab and carry out the quick detection model of near-infrared
Foundation.During model foundation, select suitable preprocessing procedures that can effectively reject in original near infrared spectrum
Ambient noise, noise of instrument and the other irrelevant informations mixed has important meaning for improving model stability and accuracy
Justice.
Using Savitzky-Golay convolution exponential smoothing, Savitzky-Golay convolution derivative method, remove Trend Algorithm, polynary
One of scatter correction method (MSC), standard normal variable converter technique (SNV), normalization method and regular method or a variety of sides
Method combines, and carries out Pretreated spectra to the near-infrared original spectrum of step (2) acquisition, removal includes ambient noise, noise of instrument
Uncorrelated noise Deng including locates above-mentioned spectrum as model-evaluation index using coefficient R and root-mean-square error Rmsec in advance
Reason method is compared, and the results are shown in Table 1.
Influence of the different preprocessing procedures of table 1 to the quick detection model of Sargassum horneri content of cellulose near-infrared
Table 1
Note: S-G is writing a Chinese character in simplified form for Savitzky-Golay, RCC、RCVRespectively represent prediction of the model to calibration set, verifying collection
Related coefficient, RmsecCC、RmsecCVModel is respectively represented to the predicted root mean square error of calibration set, verifying collection.
As seen from table, preprocessing procedures should be selected as Savitzky-Golay convolution First derivative spectrograply+trend is gone to calculate
Method.
Meanwhile to improve the ability that model differentiates specific components information, to single or a small amount of target components sample
Carry out content analysis when, can be while necessary information be lost, diminution spectral region appropriate, reject spectrum it is uncorrelated or
Non-linear variable is enriched with representative spectral band with this and weakens influence of the irrelevant information to Model Distinguish, improves
The stability and precision of prediction of model.
Using interval partial least square (IPLS), competitive adaptive weighting sampling method (CARS), correlation coefficient process, company
One of continuous sciagraphy (SPA), genetic algorithm (GA) or a variety of methods combine, to process Pretreated spectra in step (4)
Near infrared spectrum carries out characteristic wavelength screening, using coefficient R and root-mean-square error Rmsec as model-evaluation index to upper
It states characteristic wavelength screening technique to be compared, the results are shown in Table 2.
Influence of the 2 different characteristic wavelength screening technique of table to the quick detection model of Sargassum horneri content of cellulose near-infrared
Table 2
Note: RCC、RCVModel is respectively represented to the prediction related coefficient of calibration set, verifying collection, RmsecCC、RmsecCVRespectively
Predicted root mean square error of the representative model to calibration set, verifying collection.
As seen from table, cellulose model modeling wave band should be selected as 4930~4560cm-1With 5820~5640cm-1, this be by
The group sum of fundamental frequencies of nonbonding hydroxyl stretching vibration and the stretching vibration of methylene in cellulosic component are contained in the wave band, therefore
Model can be effectively improved to the identification performance of cellulosic component.
After determining preprocessing procedures and modeling wave band, the quick detection model of near-infrared is established with offset minimum binary.
Wherein, the number of main factor of selection is too small, then can lose effective information more near infrared spectrum, is fitted insufficient；It chooses
Number of main factor is too many, then will lead to that measurement noise is excessive to include, and over-fitting occurs, model predictive error can be significant
Increase.
Situation is chosen to multiple number of main factoies as model-evaluation index using coefficient R and root-mean-square error Rmsec
It is compared, the results are shown in Table 3.
Influence of the different number of main factoies of table 3 to the quick detection model of Sargassum horneri content of cellulose near-infrared
Table 3
Note: RCC、RCVModel is respectively represented to the prediction related coefficient of calibration set, verifying collection, RmsecCC、RmsecCVRespectively
Predicted root mean square error of the representative model to calibration set, verifying collection.
Comprehensively consider influence of the different number of main factoies to model prediction correlation coefficient value and root-mean-square error value, main gene
Number should be preferably 10.
After the quick detection model of content of cellulose near-infrared is established, it is based on forecast set, built Quantitative Analysis Model is carried out
Verifying.And use coefficient R and root-mean-square error Rmsec and relation analysis error RPD as evaluation index to model built
Estimated performance verified, the results are shown in Table 4.
The verification result of the 4 quick detection model of content of cellulose near-infrared of table
Table 4
The built quick detection model estimated performance of content of cellulose near-infrared is more excellent, and applicability is preferable, and Sargassum horneri cellulose contains
The chemical analysis value of amount figure related to model predication value is shown in attached drawing 3, and the chemical analysis value and model of Sargassum horneri content of cellulose are pre-
The comparison figure of measured value is shown in attached drawing 4.
(5) application of the quick detection model of content of cellulose near-infrared
Unknown kelp to be measured is subjected to sample pretreatment according to step (1), it is then corresponding according to step (2) acquisition
Near infrared spectrum, by spectroscopic data import 8.3 software of matlab, and be based on established cellulose Near-Infrared Quantitative Analysis
Model can quickly measure the content of the cellulosic component in unknown kelp to be measured.
Embodiment 2: Sargassum horneri hemicellulose level rapid detection method, process are as follows:
(1) collection and pretreatment of kelp sample
40 groups of Sargassum horneris of different waters different batches are acquired as embodiment sample, sample is by clear water washing until surface
The silt and salinity of attachment are all divided, and are subsequently placed to simply be air-dried under sunlight, are then dried, will wash at 105 DEG C
Drying Sargassum horneri sample pulverizer after net is crushed and is passed through multilayer screen cloth, and 60 mesh samples is taken to be packed into the sealing for evacuating air
It is spare in transparent bag.
(2) acquisition of kelp sample near infrared spectrum data
Weigh the rotating sample pool that kelp particle 4g is placed near infrared spectrometer, fill and strike off, by with Nicolet
IS10 Fourier Transform Near Infrared instrument (Thermo Fisher company of the U.S.) is the same as the near infrared spectrum of precision or higher precision
Instrument acquires spectrum, scanning times 64, resolution ratio 8cm under diffusing reflection mode-1, when scanning environment temperature keep constant (5~
25 DEG C), humidity is lower than 25%, and sample answers epigranular and sufficiently drying, and instrument sets automatic collection background spectrum, and spectrometer is swept
Retouching wave-number range is 12000~4000cm-1, the near infrared spectrum data repeated acquisition of each sample 3 times takes 3 spectroscopic datas
Original near infrared spectrum of the average value as corresponding brown alga sample.40 groups of Sargassum horneri particle near-infrared original spectrum scanning figures are shown in attached
Fig. 1.
(3) the wet chemical analytical method measurement of kelp hemicellulose level
Wet chemical analytical method uses Hydrolyze method, and method detailed step is as follows: weighing Sargassum horneri 0.1g (± 0.0001g) and is placed in
In 50mL beaker, the calcium nitrate solution of 10mL mass fraction 40% is added, is placed in heating in water-bath and boils 10min；It takes out cold
But to filtering after room temperature, filtrate is discarded, is collected and is all precipitated and be washed with distilled water 3 times；Precipitating is placed in 25mL test tube, to
The hydrochloric acid solution that 10mL concentration is 2mol/L is added in precipitating, shakes up the stirred in water bath heating 45min for being placed on and having boiled；With
After take out, filtered after being cooled to room temperature, collect whole filtrates and be placed in 100mL volumetric flask, precipitating rinsed 3 times, cleaning solution is same
It pours into volumetric flask；1 drop phenolphthalein indicator is added into volumetric flask, and is neutralized to the sodium hydroxide solution of 2mol/L pink
Then color is diluted to graduation mark with distilled water；Solution is filtered into beaker, initial a few drop filtrates are given up；Then 8mL is taken to filter
2mL DNS reagent is added in test tube in liquid, remembers that the concentration of reduced sugar in solution at this time is C, unit mg/mL is placed in and has boiled
10min is heated in the water-bath risen, after being cooled to room temperature, absorbance is measured under 540nm wavelength, is denoted as A.Wherein absorbance A with
Relationship between concentration of reduced sugar C meets glucose standard curve, and specific linear relationship is as follows:
A=8.319C+0.0018
Hemicellulose level is calculated as follows:
In formula: X represents hemicellulose level, %；M is weighed Sargassum horneri granular mass, g.
40 groups of Sargassum horneri particle hemicellulose level chemical analysis Distribution values are shown in attached drawing 5.
(4) foundation and verifying of the quick detection model of near-infrared
Firstly, by the near-infrared original spectral data of step (2) acquisition and three component of lignocellulosic of step (3) measurement
Content data constitutes total sample set, and the identification and rejecting of exceptional sample are carried out to total sample set, and the exceptional sample method of inspection includes
Mahalanobis distance method, concentration residual error method and Principal Component Analysis.Finally, in 40 groups of Sargassum horneri samples and sample without exception exist, be based on
Kennard-Stone (K-S) method chooses 24 groups of samples as calibration set, and 10 groups of samples are made as verifying collection and 6 groups of samples
For forecast set.
Above-mentioned calibration set and verifying collection spectroscopic data are imported 8.3 software of matlab and carry out the quick detection model of near-infrared
Foundation.During model foundation, select suitable preprocessing procedures that can effectively reject in original near infrared spectrum
Ambient noise, noise of instrument and the other irrelevant informations mixed has important meaning for improving model stability and accuracy
Justice.
Using Savitzky-Golay convolution exponential smoothing, Savitzky-Golay convolution derivative method, remove Trend Algorithm, polynary
One of scatter correction method (MSC), standard normal variable converter technique (SNV), normalization method and regular method or a variety of sides
Method combines, and carries out Pretreated spectra to the near-infrared original spectrum of step (2) acquisition, removal includes ambient noise, noise of instrument
Uncorrelated noise Deng including locates above-mentioned spectrum as model-evaluation index using coefficient R and root-mean-square error Rmsec in advance
Reason method is compared, and the results are shown in Table 5.
Influence of the different preprocessing procedures of table 5 to the quick detection model of Sargassum horneri hemicellulose level near-infrared
Table 5
Note: S-G is writing a Chinese character in simplified form for Savitzky-Golay, RCC、RCVRespectively represent prediction of the model to calibration set, verifying collection
Related coefficient, RmsecCC、RmsecCVModel is respectively represented to the predicted root mean square error of calibration set, verifying collection.
As seen from table, preprocessing procedures should be selected as Savitzky-Golay convolution First derivative spectrograply.
Meanwhile for improve ability that model differentiates specific components information to single or a small amount of target components sample into
, can be while necessary information be lost when row content analysis, it is uncorrelated or non-to reject spectrum for diminution spectral region appropriate
Linear variable is enriched with representative spectral band with this and weakens influence of the irrelevant information to Model Distinguish, improves mould
The stability and precision of prediction of type.
Using interval partial least square (IPLS), competitive adaptive weighting sampling method (CARS), correlation coefficient process, company
One of continuous sciagraphy (SPA), genetic algorithm (GA) or a variety of methods combine, to process Pretreated spectra in step (4)
Near infrared spectrum carries out characteristic wavelength screening, using coefficient R and root-mean-square error Rmsec as model-evaluation index to upper
It states characteristic wavelength screening technique to be compared, the results are shown in Table 6.
Influence of the 6 different characteristic wavelength screening technique of table to the quick detection model of Sargassum horneri hemicellulose level near-infrared
Table 6
Note: RCC、RCVModel is respectively represented to the prediction related coefficient of calibration set, verifying collection, RmsecCC、RmsecCVRespectively
Predicted root mean square error of the representative model to calibration set, verifying collection.
As seen from table, hemicellulose model modeling wave band should be selected as 5643~5340cm-1, this is because the wave band contains
The stretching vibration of the group sum of fundamental frequencies and methylene of the stretching vibration of nonbonding hydroxyl and the stretching vibration of C-O key in hemi-cellulose components,
Therefore model can be effectively improved to the identification performance of hemi-cellulose components.
After determining preprocessing procedures and modeling wave band, the quick detection model of near-infrared is established with offset minimum binary.
Wherein, the number of main factor of selection is too small, then can lose effective information more near infrared spectrum, is fitted insufficient；It chooses
Number of main factor is too many, then will lead to that measurement noise is excessive to include, and over-fitting occurs, model predictive error can be significant
Increase.
Situation is chosen to multiple number of main factoies as model-evaluation index using coefficient R and root-mean-square error Rmsec
It is compared, the results are shown in Table 7.
Influence of the different number of main factoies of table 7 to the quick detection model of Sargassum horneri hemicellulose level near-infrared
Table 7
Note: RCC、RCVModel is respectively represented to the prediction related coefficient of calibration set, verifying collection, RmsecCC、RmsecCVRespectively
Predicted root mean square error of the representative model to calibration set, verifying collection.
Comprehensively consider influence of the different number of main factoies to model prediction correlation coefficient value and root-mean-square error value, main gene
Number should be preferably 15.
After the quick detection model of hemicellulose level near-infrared is established, be based on forecast set, to built Quantitative Analysis Model into
Row verifying, and using coefficient R and root-mean-square error Rmsec and relation analysis error RPD as evaluation index to being modeled
The estimated performance of type is verified, and the results are shown in Table 8.
The verification result of the 8 quick detection model of hemicellulose level near-infrared of table
Table 8
The built quick detection model estimated performance of hemicellulose level near-infrared is more excellent, and applicability is preferable, Sargassum horneri hemicellulose
The chemical analysis value of cellulose content figure related to model predication value is shown in attached drawing 6, the chemical analysis value of Sargassum horneri hemicellulose level with
The comparison figure of model predication value is shown in attached drawing 7.
(5) application of the quick detection model of hemicellulose level near-infrared
Unknown kelp to be measured is subjected to sample pretreatment according to step (1), it is then corresponding according to step (2) acquisition
Near infrared spectrum, spectroscopic data is imported into 8.3 software of matlab, and quantitatively divide based on established hemicellulose near-infrared
Model is analysed, the content of the hemi-cellulose components in unknown kelp to be measured can be quickly measured.
Embodiment 3: Sargassum horneri content of lignin rapid detection method, process are as follows:
(1) collection and pretreatment of kelp sample
40 groups of Sargassum horneris of different waters different batches are acquired as embodiment sample, sample is by clear water washing until surface
The silt and salinity of attachment are all divided, and are subsequently placed to simply be air-dried under sunlight, are then dried, will wash at 105 DEG C
Drying Sargassum horneri sample pulverizer after net is crushed and is passed through multilayer screen cloth, and 60 mesh samples is taken to be packed into the sealing for evacuating air
It is spare in transparent bag.
(2) acquisition of kelp sample near infrared spectrum data
Weigh the rotating sample pool that kelp particle 4g is placed near infrared spectrometer, fill and strike off, by with Nicolet
IS10 Fourier Transform Near Infrared instrument (Thermo Fisher company of the U.S.) is the same as the near infrared spectrum of precision or higher precision
Instrument acquires spectrum, scanning times 64, resolution ratio 8cm under diffusing reflection mode-1, when scanning environment temperature keep constant (5~
25 DEG C), humidity is lower than 25%, and sample answers epigranular and sufficiently drying, and instrument sets automatic collection background spectrum, and spectrometer is swept
Retouching wave-number range is 12000~4000cm-1, the near infrared spectrum data repeated acquisition of each sample 3 times takes 3 spectroscopic datas
Original near infrared spectrum of the average value as corresponding brown alga sample.40 groups of Sargassum horneri particle near-infrared original spectrum scanning figures are shown in attached
Fig. 1.
(3) the wet chemical analytical method measurement of kelp content of lignin
Wet chemical analytical method uses National Renewable Energy laboratory (NREL) method, and method detailed step is as follows:
Hydrolysis bottle is added in the Sargassum horneri particle for weighing 0.3g (± 0.001g), and label does Duplicate Samples, is carried out with 200mL high purity water to sample
Soxhlet extraction, coutroi velocity carry out 6 reflux per hour, carry out 6 altogether to for 24 hours, then dry, weigh；Again with 190 ± 5mL second
Alcohol to sample carry out soxhlet extraction, coutroi velocity carry out per hour 6 times reflux, altogether carry out 16~for 24 hours, then dry, weigh；Add
Enter 72% sulfuric acid of 3.00 ± 0.01mL, stirring to raw material is sufficiently mixed, mixing be placed on heat preservation 60 in 30 ± 3 DEG C of water-bath ±
5min, every 5~10min stirring is primary, takes out addition 84 ± 0.04mL deionized water and is diluted to 4%, cover tightly knob, overturns mixed
It is even；Vacuum filter is carried out using G3 glassware after sour water solution, the deionized water (filtration time can be reduced) of heat is used when filtering
Washing filtering to residue is neutrality, collects filtrate and filter residue carries out subsequent experimental.
Filter residue is dried in baking oven (105 ± 3 DEG C), is placed in drier and is dried to over dry after taking-up, weighs note
Record (is accurate to 0.1mg, is denoted as G1)；It is transferred in Muffle furnace after drying, 575 ± 25 DEG C of 24 ± 6h of calcination measure ash content.Muffle
Furnace temperature program are as follows: room temperature is to 105 DEG C, and in 105 DEG C of reservation 12min, then 10 DEG C/min rises to 250 DEG C, retains
30min, subsequent 20 DEG C/min rises to 575 DEG C, finally retains 180min, after being cooled to 100 DEG C or less, takes out cold in drier
But (0.1g, G are accurate to weighing record after room temperature2), each sample takes parallel control.
The content of the insoluble lignin of acid is calculated as the following formula:
The insoluble content of lignin=(G of acid1-G2) × (1-Extra)/[G (1-W)] × 100%
Wherein: G1For the oven dry weight of residue and glass filter after filtering, g；G2For the ash content and glass filter after calcination
Weight, g；G is sample quality, g；W is moisture content, %；Extra represents containing for extract in high purity water and ethyl alcohol Soxhlet extraction
Amount, %.
Filtrate obtained by vacuum filter, does blank control with 4% sulfuric acid or deionized water, uses ultraviolet spectrometry after sour water solution
Photometer measurement, appropriate wavelength (recommend: measuring light absorption value under 205nm), corresponding multiple is diluted, so that its absorption value by part information
0.7~1.0.4% dilute sulfuric acid or deionized water (dilution and blank control should be consistent), absorption value weight are used when dilution
Renaturation will be ± 0.05, and each sample takes parallel control.
The content of the molten lignin of acid is calculated as the following formula:
Content=D × A × V/ (Pathlength × G) × 100% of the molten lignin of acid
Wherein: D is extension rate (liquor capacity before (liquor capacity+dilution volume before diluting)/dilution)；A is ultraviolet
Absorption value；V is the total volume of filtrate, mL；Pathlength is the optical path length of spectrophotometer, cm；G is the over dry of sample
Weight, g.
The content of lignin of final Sargassum horneri sample is the sum of the content of the insoluble content of lignin of acid and the molten lignin of acid.
40 groups of Sargassum horneri ligno cellulose content chemical analysis Distribution values are shown in attached drawing 8.
(4) foundation and verifying of the quick detection model of near-infrared
Firstly, by the near-infrared original spectral data of step (2) acquisition and three component of lignocellulosic of step (3) measurement
Content data constitutes total sample set, and the identification and rejecting of exceptional sample are carried out to total sample set, and the exceptional sample method of inspection includes
Mahalanobis distance method, concentration residual error method and Principal Component Analysis.Finally, in 40 groups of Sargassum horneri samples and sample without exception exist, be based on
Kennard-Stone (K-S) method chooses 24 groups of samples as calibration set, and 10 groups of samples are made as verifying collection and 6 groups of samples
For forecast set.
Above-mentioned calibration set and verifying collection spectroscopic data are imported 8.3 software of matlab and carry out the quick detection model of near-infrared
Foundation.During model foundation, select suitable preprocessing procedures that can effectively reject in original near infrared spectrum
Ambient noise, noise of instrument and the other irrelevant informations mixed has important meaning for improving model stability and accuracy
Justice.
Using Savitzky-Golay convolution exponential smoothing, Savitzky-Golay convolution derivative method, remove Trend Algorithm, polynary
One of scatter correction method (MSC), standard normal variable converter technique (SNV), normalization method and regular method or a variety of sides
Method combines, and carries out Pretreated spectra to the near-infrared original spectrum of step (2) acquisition, removal includes ambient noise, noise of instrument
Uncorrelated noise Deng including locates above-mentioned spectrum as model-evaluation index using coefficient R and root-mean-square error Rmsec in advance
Reason method is compared, and the results are shown in Table 9.
Influence of the different preprocessing procedures of table 9 to the quick detection model of Sargassum horneri content of lignin near-infrared
Table 9
Note: S-G is writing a Chinese character in simplified form for Savitzky-Golay, RCC、RCVRespectively represent prediction of the model to calibration set, verifying collection
Related coefficient, RmsecCC、RmsecCVModel is respectively represented to the predicted root mean square error of calibration set, verifying collection.
As seen from table, preprocessing procedures should be selected as Savitzky-Golay convolution exponential smoothing.
Meanwhile to improve the ability that model differentiates specific components information, to single or a small amount of target components sample
Carry out content analysis when, can be while necessary information be lost, diminution spectral region appropriate, reject spectrum it is uncorrelated or
Non-linear variable is enriched with representative spectral band with this and weakens influence of the irrelevant information to Model Distinguish, improves
The stability and precision of prediction of model.
Using interval partial least square (IPLS), competitive adaptive weighting sampling method (CARS), correlation coefficient process, company
One of continuous sciagraphy (SPA), genetic algorithm (GA) or a variety of methods combine, to process Pretreated spectra in step (4)
Near infrared spectrum carries out characteristic wavelength screening, using coefficient R and root-mean-square error Rmsec as model-evaluation index to more
A characteristic wavelength screening technique is compared, and the results are shown in Table 10.
Influence of the 10 different characteristic wavelength screening technique of table to the quick detection model of Sargassum horneri content of lignin near-infrared
Table 10
Note: RCC、RCVModel is respectively represented to the prediction related coefficient of calibration set, verifying collection, RmsecCC、RmsecCVRespectively
Predicted root mean square error of the representative model to calibration set, verifying collection.
As seen from table, lignin model modeling wave band should be selected as 5377~4824cm-1And 7405~6194cm-1, this is
Since the wave band contains the level-one frequency multiplication of nonbonding hydroxyl stretching vibration in lignin component, hydroxyl polymeric body stretching vibration
Level-one frequency multiplication and the group sum of fundamental frequencies of the stretching vibration of nonbonding hydroxyl and the stretching vibration of C-O key, can effectively improve model to wood
The identification performance of quality component.
After determining preprocessing procedures and modeling wave band, the quick detection model of near-infrared is established with offset minimum binary.
Wherein, the number of main factor of selection is too small, then can lose effective information more near infrared spectrum, is fitted insufficient；It chooses
Number of main factor is too many, then will lead to that measurement noise is excessive to include, and over-fitting occurs, model predictive error can be significant
Increase.
Situation is chosen to multiple number of main factoies as model-evaluation index using coefficient R and root-mean-square error Rmsec
It is compared, the results are shown in Table 11.
Influence of the different number of main factoies of table 11 to the quick detection model of Sargassum horneri content of lignin near-infrared
Table 11
Note: RCC、RCVModel is respectively represented to the prediction related coefficient of calibration set, verifying collection, RmsecCC、RmsecCVRespectively
Predicted root mean square error of the representative model to calibration set, verifying collection.
Comprehensively consider influence of the different number of main factoies to model prediction correlation coefficient value and root-mean-square error value, main gene
Number should be preferably 15.
After the quick detection model of content of lignin near-infrared is established, it is based on forecast set, built Quantitative Analysis Model is carried out
Verifying.And use coefficient R and root-mean-square error Rmsec and relation analysis error RPD as evaluation index to model built
Estimated performance verified, the results are shown in Table 12.
The verification result of the 12 quick detection model of content of lignin near-infrared of table
Table 12
The built quick detection model estimated performance of content of lignin near-infrared is more excellent, and applicability is preferable, and Sargassum horneri lignin contains
The chemical analysis value of amount figure related to model predication value is shown in attached drawing 9, and the chemical analysis value and model of Sargassum horneri content of cellulose are pre-
The comparison figure of measured value is shown in attached drawing 10.
(5) application of the quick detection model of content of lignin near-infrared
Unknown kelp to be measured is subjected to sample pretreatment according to step (1), it is then corresponding according to step (2) acquisition
Near infrared spectrum, by spectroscopic data import 8.3 software of matlab, and be based on established lignin Near-Infrared Quantitative Analysis
Model can quickly measure the content of the lignin component in unknown kelp to be measured.
It should be noted that preferred embodiment above is only embodiments of the present invention it is more readily appreciated that rather than to limit
The fixed present invention.Although the present invention has been described in detail by above preferred embodiment, belonging to any present invention
It is in technical field it will be appreciated by the skilled person that the modification and change of certain limit can be made in the formal and details of implementation
Change, without departing from claims of the present invention limited range.
The present invention proposes a kind of side that three constituent content of kelp lignocellulosic is quickly detected based near infrared spectrum
Method, it is comprehensive using method in embodiment 1,2,3, can be realized in kelp the content of three component of lignocellulosic it is quick
Nondestructive analysis, compared with conventional wet chemical quantitative analysis method and modern chromatographic method, not only have detection is quick, result is accurate,
The advantages that process is environmentally friendly, low in cost, while in the biomass conversion processes using kelp as raw material, be conducive to be promoted big
The quality control level of type brown alga biomass components content has one to the optimization of the quality control processes of other marine biomass
Determine reference.
Claims (4)
1. a kind of method for quickly being detected three constituent content of kelp lignocellulosic based near infrared spectrum, feature are existed
In the described method comprises the following steps:
The first step, the collection and pretreatment of kelp sample
The kelp sample for acquiring the multiple batches of different waters is washed with clear water to the silt and salinity for eliminating surface attachment
After be placed in preliminarily dried under sunlight, be then further dried in baking oven, most afterwards after pulverizer crushes use multi-deck screen mesh screen
Point, take 60 mesh brown alga particles to be fitted into spare in the sealed transparent bag vacuumized；
Second step, the acquisition of kelp sample near infrared spectrum data
The rotating sample pool that kelp particle is placed near infrared spectrometer is weighed, fills and strikes off, is acquired under diffusing reflection mode
Spectroscopic data, spectrometer scanning times are 64, resolution ratio 8cm-1, scanning wave-number range is 12000~4000cm-1, each sample
The near infrared spectrum data repeated acquisition of product is multiple, takes the average value of multiple spectroscopic data as the original close of corresponding brown alga sample
Infrared spectroscopy；
Third step, the wet chemical analytical method measurement of three constituent content of lignocellulosic in kelp sample
Using the content of cellulose of improved sulfuric acid and potassium dichromate oxidation measurement kelp sample, surveyed using Hydrolyze method
The hemicellulose level for determining kelp sample measures kelp using National Renewable Energy laboratory NREL method
The content of lignin of sample；
4th step, the foundation and verifying of the quick detection model of near-infrared
Firstly, by the near-infrared original spectral data of second step acquisition and the three constituent content number of lignocellulosic of third step measurement
According to total sample set is constituted, the identification and rejecting of exceptional sample are carried out to total sample set, remaining sample set uses Kennard-Stone
Method is divided into calibration set, verifying collection and forecast set.Be then based on calibration set and verifying collect, to near-infrared original spectral data into
Row Pretreated spectra and characteristic wavelength screen, and it is quick to use Partial Least Squares PLS to establish content of cellulose near-infrared respectively
Detection model, the quick detection model of hemicellulose level near-infrared, the quick detection model of content of lignin near-infrared, are finally based on
Forecast set verifies institute's quick detection model of near-infrared, wherein using coefficient R, root-mean-square error Rmsec, opposite
Evaluation index of the analytical error RPD as model prediction performance；
5th step, the application of the quick detection model of near-infrared
Unknown kelp sample to be measured is subjected to sample pretreatment according to the first step, is then adopted according to the second step
Collect corresponding near infrared spectrum data, near infrared spectrum data is inputted into established near-infrared in the 4th step and is quickly detected
In model, the three constituent content data of lignocellulosic of kelp sample to be measured can be obtained.
2. one kind according to claim 1 quickly detects three component of kelp lignocellulosic based near infrared spectrum and contains
The method of amount, which is characterized in that in the 4th step, the preferred parameter of the built quick detection model of content of cellulose near-infrared is such as
Under:
(a) preprocessing procedures are Savitzky-Golay convolution First derivative spectrograply (1st)+remove Trend Algorithm, wherein
The parameter of Savitzky-Golay convolution First derivative spectrograply are as follows: window width 331cm-1, fitting of a polynomial order is 3；It goes
The parameter of gesture algorithm are as follows: fitting of a polynomial order is 1；
(b) characteristic wavelength screening technique be interval partial least square IPLS+ Genetic Algorithms, modeling wave band be 4930~
4560cm-1With 5820~5640cm-1；
(c) number of main factor selected by Partial Least Squares PLS is 10.
3. one kind according to claim 1 quickly detects three component of kelp lignocellulosic based near infrared spectrum and contains
The method of amount, which is characterized in that in the 4th step, the preferred parameter of the built quick detection model of hemicellulose level near-infrared
It is as follows:
(a) preprocessing procedures are Savitzky-Golay convolution First derivative spectrograply (1st), wherein Savitzky-Golay
The parameter of convolution First derivative spectrograply are as follows: window width 201cm-1, fitting of a polynomial order is 3；
(b) characteristic wavelength screening technique is interval partial least square IPLS, and modeling wave band is 5643~5340cm-1；
(c) number of main factor selected by Partial Least Squares PLS is 15.
4. one kind according to claim 1 quickly detects three component of kelp lignocellulosic based near infrared spectrum and contains
The method of amount, which is characterized in that the preferred parameter of the built quick detection model of content of lignin near-infrared is such as in the 4th step
Under:
(a) preprocessing procedures are Savitzky-Golay convolution exponential smoothing, wherein Savitzky-Golay convolution exponential smoothing
Parameter are as follows: window width 231cm-1, fitting of a polynomial order is 3；
(b) characteristic wavelength screening technique is correlation coefficient process, and modeling wave band is 5377~4824cm-1And 7405~6194cm-1；
(c) number of main factor selected by Partial Least Squares PLS is 15.
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